Spatial distribution characteristics and prediction of fluorine concentration in groundwater based on driving factors analysis

Critical role of vegetation and human activity indicators in the prediction of shallow groundwater quality distribution in Jianghan Plain with LightGBM algorithm and SHAP analysis

Groundwater serves as an indispensable resource for freshwater, but its quality has experienced a notable decline over recent decades. Spatial prediction of groundwater quality (GWQ) can effectively assist managers in groundwater remediation, management, and risk control. Based on the traditional intrinsic groundwater vulnerability (IGV) model (DRASTIC) and three vegetation (V) indicators (NDVI, EVI, and kNDVI) and four human activity (H) indicators (land use, GDP, urbanization index, and nighttime light), we constructed four models for GWQ spatial prediction in the Jianghan Plain (JHP), namely DRASTI, DRASTIH, DRASTIV, and DRASTIVH, excluding the conductivity (C) indicator due to its uniformly low values. LightGBM algorithm, Tree-structured Parzen Estimator (TPE) optimization method, and SHapley Additive exPlanations (SHAP) analysis are used for model setting, calibration, and interpretation, respectively. The results show that nitrogen-related GWQ parameters have higher weights, and the model performs exceptionally well when considering all the indicators (accuracy = 0.840, precision = 0.824, recall = 0.832, F1 score = 0.828, AUROC = 0.914). Notably, the introduced indicators (NDVI, EVI, kNDVI, nighttime light, GDP, and urbanization index) rank as the top six in terms of importance, while traditional DRASTI and land use indicators show lower significance. Based on SHAP analysis, poor GWQ primarily occurs in areas with either extremely high or extremely low GDP and urbanization index values, and human activities are the primary cause of poor GWQ in JHP, potentially involving urbanization, industrial and agricultural activities, as well as fertilizer usage. Finally, the methodological framework proposed in this study is encouraged to be applied to diverse regions, such as plains, karst areas, mountainous regions, and coastal areas, to support effective future groundwater management.

Recent advances in groundwater pollution research using machine learning from 2000 to 2023: A bibliometric analysis

Groundwater pollution has become a global challenge, posing significant threats to human health and ecological environments. Machine learning, with its superior ability to capture non-linear relationships in data, has shown significant potential in addressing groundwater pollution issues. This review presents a comprehensive bibliometric analysis of 1462 articles published between 2000 and 2023, offering an overview of the current state of research, analyzing development trends, and suggesting future directions. The analysis reveals a growing trend in publications over the 24-year period, with a sharp expansion since 2020. China, the USA, India, and Iran are identified as the leading contributors to publications and citations, with prominent institutions such as Jilin University, the United States Geological Survey, and the University of Tabriz. Moreover, keyword frequency analysis indicates that principal component analysis (PCA) is the most commonly used method, followed by artificial neural network (ANN) and hierarchical clustering analysis (HCA). The most studied groundwater pollutants include nitrate, arsenic, heavy metals, and fluoride. As machine learning has rapidly advanced, research focuses have evolved from fundamental tasks like hydrochemical evolution analysis, water quality index evaluation, and groundwater vulnerability assessments to more complex issues, such as pollutant concentration prediction, pollution risk assessment, and pollution source identification. Despite these advances, challenges related to data quality, data scarcity, model generalization, and interpretability remain. Future research should prioritize data sharing, improving model interpretability, broadening research horizons and advancing theory-guided machine learning. These will enhance our understanding of groundwater pollution mechanisms, and ultimately facilitate more effective pollution control and remediation strategies. In summary, this review provides valuable insights and suggestions for researchers and policymakers working in this critical field.

Evaluating major element hydrogeochemistry and fluoride occurrence in groundwater of crystalline bedrock aquifers and associated controlling factors of Eumseong basin area, South Korea

Long-term intake of high-fluoride water can cause fluorosis in bones and teeth or damage to organs. Fluoride in groundwater is primarily derived from reactions with rocks containing fluorine-related minerals, and fluoride concentrations are elevated in groundwater that has been reacting with these rocks for a long time. The purpose of this study is to investigate the origin and distribution of fluoride in groundwater and to assess the influence of various factors, including geology, on fluoride concentrations in groundwater. The Eumseong basin and surrounding areas were selected as the study area due to the diversity of geologic factors. 139 groundwater samples and 14 rock samples were collected, with groundwater samples subjected to field water quality measurements, chemical analysis, and statistical analysis, and rock samples subjected to microscopic observation and chemical analysis. Fluoride concentration in groundwater increased with well depth, and was highest in groundwater associated with granitic rocks rich in biotite. The fluoride concentration in groundwater showed a negative correlation with the distance to the fault, suggesting that deep groundwater may preferentially flow along fault zones in certain areas. In addition, high-fluoride groundwater had depleted water-stable isotope values, which is likely to be resulted from higher degree of water-rock interaction in groundwater recharged at higher elevations. Calcite precipitation in most groundwater appears to weaken fluorite solubility control on fluoride concentration. Multivariate statistical analysis revealed that water-rock interactions generally governed fluoride and major element concentrations, with high-fluoride groundwater clearly distinguished. These findings can aid in assessing fluoride occurrence in groundwater and managing water quality in areas with similar geological characteristics.

Geogenic fluorine-contaminated groundwater increases fluorosis risk in communities of northern cold regions: Genesis mechanism and exposure pathways

Geogenic fluorine-contaminated groundwater (F >1 mg/L) prevails in cold Mollisol regions of the world. Seasonal variation of F concentration in groundwater likely renders multiple pathways of toxic-level F exposure, posing unrecognized health risk to many economically challenged communities. Herein, different types of samples within the groundwater-soil-crop-human hair network were collected from the Mollisol regions of northeastern China and assessed by joint approach of medical geochemical assay, hydrogeochemical modeling, and health risk indexation. The results unravel that infiltration of dissolved organic matter from Mollisols induced by vertical infiltration led to seasonal variation of F concentration and speciation in groundwater. This is attributable primarily to biogenic dissolution-precipitation equilibria of Ca-containing minerals and altered hydrochemical types of groundwater over season, causing dynamic F- partitioning between water and minerals in aquifers. Further risk assessment suggests greater adverse effects of groundwater use on human health in summer than in autumn. Especially, two major pathways of F exposure, i.e., groundwater drinking-human and groundwater irrigation-soil-crop-human, accounted for 57.3 % of F accumulation in hair of local residents. This research highlights the underestimated health impacts of seasonal variability of F in exploited groundwater and the urgency of groundwater quality management to prevent endemic fluorosis in communities of cold regions.

Seasonal dynamics of dissolved inorganic nitrogen in groundwater: Tracing environmental controls and land use impact

High levels of dissolved inorganic nitrogen (DIN) in groundwater pose challenges for regions like northern Anhui Province, China, where groundwater is a crucial domestic resource. This study utilized modern geostatistics to explore the spatial and temporal dynamics of DIN in groundwater. Significant seasonal influences on DIN concentrations were identified: ammonium peaks during wet season driven by agricultural activities, while nitrate peaks during the dry season primarily influenced by municipal inputs. This study established a Bayesian Maximum Entropy - Random Forest (BME-RF) model based on Land Use/Land Cover data to infer the spatio-temporal performance of DIN, achieving an interpretation rate above 90 %. It also highlighted the role of hydrogeological conditions and aquifer types in the evolution of DIN. By employing a DIN environmental interaction model, it further analyzed the eco-hydrological drivers and seasonal trends affecting DIN variability, enhancing the understanding of groundwater nitrogen dynamics and their link to environmental factors with low consumption. SYNOPSIS: This study reveals seasonal shifts in groundwater DIN, links them to human activity, and uses the BME model to guide targeted nitrogen fluctuation.

Predicting groundwater phosphate levels in coastal multi-aquifers: A geostatistical and data-driven approach

The groundwater (GW) resource plays a central role in securing water supply in the coastal region of Bangladesh and therefore the future sustainability of this valuable resource is crucial for the area. However, there is limited research on the driving factors and prediction of phosphate concentration in groundwater. In this work, geostatistical modeling, self-organizing maps (SOM) and data-driven algorithms were combined to determine the driving factors and predict GW phosphate content in coastal multi-aquifers in southern Bangladesh. The SOM analysis identified three distinct spatial patterns: K+Na+pH, Ca2+Mg2+NO₃-, and HCO₃-SO₄2-PO43-F-. Four data-driven algorithms, including CatBoost, Gradient Boosting Machine (GBM), Long Short-Term Memory (LSTM), and Support Vector Regression (SVR) were used to predict phosphate concentration in GW using 380 samples and 15 prediction parameters. Forecasting accuracy was evaluated using RMSE, R2, RAE, CC, and MAE. Phosphate dissolution and saltwater intrusion, along with phosphorus fertilizers, increase PO43- content in GW. Using input parameters selected by multicollinearity and SOM, the CatBoost model showed exceptional performance in both training (RMSE = 0.002, MAE = 0.001, R2 = 0.999, RAE = 0.057, CC = 1.00) and testing (RMSE = 0.001, MAE = 0.002, R2 = 0.989, RAE = 0.057, CC = 0.998). Na+, K+, and Mg2+ significantly influenced prediction accuracy. The uncertainty study revealed a low standard error for the CatBoost model, indicating robustness and consistency. Semi-variogram models confirmed that the most influential attributes showed weak dependence, suggesting that agricultural runoff increases the heterogeneity of PO43- distribution in GW. These findings are crucial for developing conservation and strategic plans for sustainable utilization of coastal GW resources.

Groundwater suitability assessment for irrigation and drinking purposes by integrating spatial analysis, machine learning, water quality index, and health risk model

An in-depth understanding of nitrate-contaminated surface water and groundwater quality and associated risks is important for groundwater management. Hydrochemical characteristics and driving forces of groundwater quality and non-carcinogenic risks of nitrate were revealed by the integrated approaches of self-organizing map analysis, spatial visualization by geography information system, entropy and irrigation water quality indices, and human health risk model. Groundwater samples were categorized into two clusters by SOM analysis. Cluster I including three samples were Ca-SO4 type and cluster II of remaining 136 samples were Ca-HCO3 type. Hydrochemical compositions of two cluster samples were dominated by water-rock interaction: (1) calcite and gypsum dissolution for cluster I samples and (2) calcite dissolution, silicate weathering, and positive cation exchange for cluster II samples. Nitrate contamination occurred in both cluster I and II samples, primarily induced by agricultural nitrogen fertilizer. The EWQI results showed that 90.97% in total groundwater samples were suitable for drinking purpose, while the IWQI results demonstrated that 65.03% in total groundwater samples were appropriate for irrigation purpose. The HHR model and Monte Carlo simulation indicated that the non-carcinogenic nitrated risk was highest in children. Exposure frequency was the most sensitive factor (86.33% in total) influencing the total non-carcinogenic risk, indicated by sensitivity analysis. Compared with the two clusters of groundwater, surface water has a shorter circulation cycle and lower ion concentrations resulting in better water quality. This study can provide scientific basis for groundwater quality evaluation in other parts of the world.

Prediction of sulfate concentrations in groundwater in areas with complex hydrogeological conditions based on machine learning

The persistent and increasing levels of sulfate due to a variety of human activities over the last decades present a widely concerning environmental issue. Understanding the controlling factors of groundwater sulfate and predicting sulfate concentration is critical for governments or managers to provide information on groundwater protection. In this study, the integration of self-organizing map (SOM) approach and machine learning (ML) modeling offers the potential to determine the factors and predict sulfate concentrations in the Huaibei Plain, where groundwater is enriched with sulfate and the areas have complex hydrogeological conditions. The SOM calculation was used to illustrate groundwater hydrochemistry and analyze the correlations among the hydrochemical parameters. Three ML algorithms including random forest (RF), support vector machine (SVM), and back propagation neural network (BPNN) were adopted to predict sulfate levels in groundwater by using 501 groundwater samples and 8 predictor variables. The prediction performance was evaluated through statistical metrics (R2, MSE and MAE). Mine drainage mainly facilitated increase in groundwater SO42- while gypsum dissolution and pyrite oxidation were found another two potential sources. The major water chemistry type was Ca-HCO3. The dominant cation was Na+ while the dominant anion was HCO3-. There was an intuitive correlation between groundwater sulfate and total dissolved solids (TDS), Cl-, and Na+. By using input variables identified by the SOM method, the evaluation results of ML algorithms showed that the R2, MSE and MAE of RF, SVM, BPNN were 0.43-0.70, 0.16-0.49 and 0.25-0.44. Overall, BPNN showed the best prediction performance and had higher R2 values and lower error indices. TDS and Na+ had a high contribution to the prediction accuracy. These findings are crucial for developing groundwater protection and remediation policies, enabling more sustainable management.

Analysis of the distribution across media, migration, and related driving factors of fluoride in cold and arid lakes during the freezing period

Fluoride pollution in water has become a global challenge. This challenge especially affects China as a country experiencing serious fluoride pollution. While the have been past studies on the spatial distribution of fluoride, there has been less attention on different forms of fluoride. This study collected 176 samples (60, 40, and 76 ice, water, and sediment samples, respectively) from Lake Ulansuhai during the freezing period. The occurrence and spatial distribution characteristics of fluoride in lake ice-water-sediment were explored using Kriging interpolation, Piper three-line diagram, and Gibbs diagram analysis methods. The migration and transformation of fluoride during the freezing period were revealed and the factors influencing fluoride concentration in the water body were discussed considering the hydrochemical characteristics of lake surface water. The results showed that the average fluoride concentrations in the upper ice, middle ice and lower ice were 0.18, 0.09, and 0.12 mg/L, respectively, decreasing from north to south in the lake. The average concentrations of fluoride in surface water and bottom water were 0.63 and 0.83 mg/L, respectively. The concentrations of fluoride in ice and water were within the World Health Organisation drinking water threshold of 1.50 mg/L and the Class III Chinese surface water standard (GB3838-2002). The average sediment total fluorine was 1344.38 ± 200 mg/kg, significantly exceeding the global average (321 mg/kg) and decreasing with depth. The contents of water soluble, exchangeable, Fe/Mn bound, organic bound, and residual fluorides were 40.22-47.18, 13.24-43.23, 49.52-160.48, and 71.59-173.03 mg/kg, respectively. There was a significant positive correlation between fluoride concentration in ice and that in water. The change in fluoride concentration in water was mainly due to specific climatic and geographical conditions, pH, hydrochemical characteristics and ice sealing. This study is of great significance for the management of high-fluorine lakes in arid and semi-arid areas.

A new insight into fluoride induces cardiotoxicity in chickens: Involving the regulation of PERK/IRE1/ATF6 pathway and heat shock proteins

Fluorosis poses a significant threat to human and animal health and is an urgent public safety concern in various countries. Subchronic exposure to fluoride has the potential to result in pathological damage to the heart, but its potential mechanism requires further investigation. This study investigated the effects of long-term exposure to sodium fluoride (0, 500, 1000, and 2000 mg/kg) on the hearts of chickens were investigated. The results showed that an elevated exposure dose of sodium fluoride led to congested cardiac tissue and disrupted myofiber organisation. Sodium fluoride exposure activated the ERS pathways of PERK, IRE1, and ATF6, increasing HSP60 and HSP70 and decreasing HSP90. The NF-κB pathway and the activation of TNF-α and iNOS elicited an inflammatory response. BAX, cytc, and cleaved-caspase3 were increased, triggering apoptosis and leading to cardiac injury. The abnormal expression of HSP90 and HSP70 affected the stability and function of RIPK1, RIPK3, and MLKL, which are crucial necroptosis markers. HSPs inhibited TNF-α-mediated necroptosis and apoptosis of the death receptor pathway. Sodium fluoride resulted in heart injury in chickens because of the ERS and variations in HSPs, inducing inflammation and apoptosis. Cardiac-adapted HSPs impeded the activation of necroptosis. This paper may provide a reference for examining the potential cardiotoxic effects of sodium fluoride.

Hydrochemistry, quality, and integrated health risk assessments of groundwater in the Huaibei Plain, China

Groundwater is an essential freshwater resource utilized in industry, agriculture, and daily life. In the Huaibei Plain (HBP), where groundwater significantly influences socio-economic development, information about its quality, hydrochemistry, and related health risks remains limited. We conducted a comprehensive groundwater sampling in the HBP and examined its rock characteristics, water quality index (WQI), and potential health risks. The results revealed that the primary factors shaping groundwater hydrochemistry were rock dissolution and weathering, cation exchange, and anthropogenic activities. WQI assessment indicated that only 73% of the groundwaters is potable, as Fe2+, Mn2+, NO3-, and F- contents in the water could pose non-carcinogenic hazards to humans. Children were more susceptible to these health risks through oral ingestion than adults. Uncertainty analysis indicated that the probabilities of non-carcinogenic risk were approximately 57% and 31% for children and adults, respectively. Sensitivity analysis further identified fluoride as the primary factor influencing non-carcinogenic risks, indicating that reducing fluoride contamination should be prioritized in future groundwater management in the HBP.